Let’s dive into crucial libraries in R and Python to visualise data and create different charts, and what the professionals and cons are
Being a professional in certain programming languages is the goal of each aspiring data skilled. Reaching a certain level in considered one of the countless languages is a critical milestone for everybody.
For data engineers, SQL might be crucial language. As an online developer, it’s essential to know JavaScript, HTML, CSS and PHP in your sleep. For data scientists, however, Python and R are the popular tools. Each languages have their strengths and weaknesses — and each offer powerful tools and a big community to investigate and visualize data.
For those who’re on the very starting of your data science journey, the selection between R and Python could be overwhelming. But when you must move into this field in the long run, you’ll come into contact with each languages ultimately anyway. Also, when you’re already at university, you almost certainly have courses in each languages.
But let’s dive into crucial libraries in R and Python to visualise data, how creating charts in R and Python is different (with code examples), and what the professionals and cons of the 2 languages are.
Table of Content
1 — What makes R essential? (And essential libraries for visualizations)
2 —…
